Dynamic Behavior Analysis of Touchdown Process in Active Magnetic Bearing System Based on a Machine Learning Method

Abstract

Magnetic bearings are widely applied in High Temperature Gas-cooled Reactor (HTGR) and auxiliary bearings are important backup and safety components in AMB systems. The performance of auxiliary bearings significantly affects the reliability, safety, and serviceability of the AMB system, the rotating equipment, and the whole reactor. Research on the dynamic behavior during the touchdown process is crucial for analyzing the severity of the touchdown. In this paper, a data-based dynamic analysis method of the touchdown process is proposed. The dynamic model of the touchdown process is firstly established. In this model, some specific mechanical parameters are regarded as functions of deformation of auxiliary bearing and velocity of rotor firstly; furthermore, a machine learning method is utilized to model these function relationships. Based on the dynamic model and the Kalman filtering technique, the proposed method can offer estimation of the rotor motion state from noisy observations. In addition, the estimation precision is significantly improved compared with the method without learning. The proposed method is validated by the experimental data from touchdown experiments.

Performance evaluation of touchdown bearing using model-based approach

“…Recently, Kang and Palazzolo [14] studied the 1/2X forward whiling during the dropdown of the rotor by numerical simulation and experiments. In the study of Sun et al [35,34] the Kalman filtering technique have been used to estimate rotor's displacements, velocities and accelerations from noisy observations. Generally, industrial AMB machines do not have force sensors for touchdown bearings, and the system is only equipped with the position sensors to record the rotor displacement.…”